Driving the Digital Transformation in China's Insurance Industry

The insurance industry is adopting new technologies to improve its products and operations. This article describes some of the major advancements in China's insurance industry.

The Evolving Insurance Landscape

In the recent years, the insurance industry has enjoyed rapid growth. Despite this growth, many pain points remain unsolved and might need urgent attention. The insurance industry suffers from difficulties in the research and design of new products. There is a high prevalence of homogenization of insurance products pricing, with a very little scope for customization. The marketing and sales of insurance products are mostly dependent on intermediary channels. This form of promotion is not effective as in many cases consumers aren't able to develop a proper understanding of insurance products.

When it comes to insurance payouts, current insurance fraud prevention data and techniques are lacking. This is because they are mainly relying on offline intermediary links for prevention of insurance frauds. Moreover, the present, insurance claim procedures are very complicated. Insurance providers often fail to achieve a decent balance between good user experience and lower service costs. For claims, consumers need to provide a set of claims related material offline and have to go through a tedious multi-layer process. Further, insurance companies have traditionally managed all these claims settlements with time-consuming manual processes. These pain-points leave a room for improvement and create opportunities for the development of the insurance industry.

The Rise of InsurTech

The union between cutting-edge technologies such as artificial intelligence and the insurance industry has triggered a worldwide wave of "InsurTech" services and products. Similar to FinTech, face recognition technology for insurance can support identity verification to reduce the risk of fraud. Big Data + machine learning algorithms can help insurers in understanding customer needs. This will help them in designing online products, with accurate pricing. Further, it will help insurers in recommending relevant content and mitigate risks. Artificial Intelligence through speech and image recognition, machine learning, and natural language processing can streamline customer service, insurance, loss claims, and other processes. These technologies can significantly enhance the user experience and reduce organizational costs.

Differentiated Pricing to Meet Customer Needs

With the improvement of people's living standards, the demand for insurance products for multi-level protection is on the rise. From the supply-side perspective, however, there are very few products on the market. It is not rare for an insurance company to develop thousands of insurance products. However, very few of these products are available in the market, and only a few of them sell well. Many insurance companies cannot scientifically create differentiated pricing. There are some organizations, which blindly follow others when it comes to pricing. This leads them to rely on the cost of disguised return fees and other means to carry out the vicious price war competition. Usually, there is little regard for what the customers might want or deserve. At present, the insurance pricing problem has become one of the bottlenecks restricting the development of the insurance industry.

The development of Big Data, artificial intelligence, and other technologies gives insurance companies a new opportunity to conduct scientific and rational differentiated pricing. Insurance companies can combine basic information such as the policyholder's living habits, age, and insurance experience with artificial intelligence to mine their insurance preferences. Insurers can improve their delivery strategy and create portfolio programs for each consumer with differentiated pricing.

For example, Big Data and artificial intelligence will profoundly affect the actuarial pricing of life insurance as it has already lasted for hundreds of years. This will make insurance services more accurate and suitable for different individuals in different age groups. As another example, current travel accident insurance products rarely differentiate travel destinations in their pricing. In reality, the risks customers face in different countries vary quite a lot. A customer whose travel destination is in the United States might require an increase in the amount of medical accident insurance to cover higher medical costs. Similarly, in European countries that have socialized medicine, customers can increase the proportion of property loss protections. Big Data and Artificial Intelligence deliver differentiated products and pricing strategies by continuously tracking customer's trips.

Auto insurance has the largest share of China's insurance market. The traditional auto insurance pricing model mainly relies on the information based on the car itself, which includes the model, age, and configuration. For example, the basic premiums for a BMW and a Jetta are very different. Even within BMW, the premiums for series 3 and series 7 vary a lot. But on the other hand, there is no detailed information about the BMW owners who met accidents. The development of technologies such as Big Data and artificial intelligence enables insurance companies to further leverage the consumer data and analyze the probable risk exposure of vehicle owners. Therefore, risk factors for auto insurance can shift from a "car-oriented" approach to a "car/owner combination."

Ant Financial, Peace, CPIC, and other companies have launched various levels of exploration in this regard. For example, Ant Financial in May of this year introduced Auto Insurance Points. This points-system uses Big Data and AI to help insurance companies identify customer risks and reasonably set prices. Ant's Auto Insurance Points consider the vehicle owner's profession, credit history, spending habits, driving habits, and other relevant information. All this when combined, forms an accurate portrait of the owner and provides better risk analysis. The result is a score that ranges between 300 and 700. The higher the car owner's score, the lower is the risk of an accident.

Auto Insurance Points are a big step forward in auto insurance pricing. By accurately predicting the behavior of car owners, it provides support for accurate pricing in auto insurance. Therefore, it further protects the interests of premium customers. From the perspective of market players, it helps small to medium-sized insurance companies in overcoming their data processing shortcomings. This eventually helps them in offering more competitive pricing.

The main services of the insurance industry include after-sales claims, renewal, and customer advisory services. For handling claims, for a long time, limited by technical means, insurance companies relied mostly on manual loss-fixing. This is historically important. However, with the rapid development of the insurance market, the number of claims has increased rapidly. The conventional loss-fixing model is now proving to be inefficient. Long processing time, difficulties in management, large judgment errors, and possibilities of joint fraud burdens insurance companies, especially small to medium-sized insurance companies.

Therefore, many companies are now exploring remote loss determination with technology. However, subject to the technical bottleneck, the accuracy of the loss determination has been less than ideal. Since the beginning of this year, breakthroughs in AI such as deep learning and image recognition have significantly enhanced the accuracy and automation of remote loss determination.

Take the example Ant Financial's Ding Sun Bao. It takes photos of accidents and analyzes the damage using AI technologies such as image recognition. After a series of processes such as agile restoration, reflection reduction, and self-determined cloud-based learning, the system can finish damage determination within a few seconds. Detailed analysis includes damaged parts, repair plan, and impacts on premiums in the years after the accident. In testing, Ant Financial's Ding Sun Bao has the same accuracy as experts with around a decade of experience in the industry. Based on the current usage, for cases of property loss only, it has dramatically lowered human and time costs. Automation reduces customers' wait time and raises their satisfaction level. It can help insurance companies, especially those who are newly established, to quickly enhance their claims processes.

In addition to the field of auto insurance, out-patient insurance can also benefit from these innovative technologies. For most of the offline claims process, an online portal can be of help. Customers would first upload documents and invoices; then the machine will filter out the correct documents based on image classifiers, OCR and NLP technology. Post this automatic verification, the case will pass through the loss compensation rules. The Big Data risk model can determine whether the company should compensate the customer and for how much. In the future, we can imagine same day submission and compensation, or even real-time compensation.

For managing customer advisory services such as insurance product shopping guides, customer guides, and claims consulting, most of them currently rely on manual interaction. This includes online assistants or phone calls. This usually hampers customer experience due to limited resources on the part of the insurance company and/or delayed feedback loops. At the same time, the insurance company faces high labor costs. Chatbots that use an information database, NLP, and machine learning will answer the majority of customer inquiries online, can be available 24/7. Over a period, these chatbots will improve their ability by actively learning. Chatbots can solve the user experience problem at a lower operational cost for insurance companies.

For a long time, the use of asymmetric information to cheat the system, to steal insurance policyholders' information, or to make false claims has had a serious impact the health of the insurance industry. For example, in the United States, as high as 20% of all claims are fraud claims, and the total amount of annual insurance fraud is about $85 billion to $120 billion. In China, car manufacturers may suffer fraud in as many as 34% of cases. Such frauds raise the cost of industry claims and undermine the interests of the properly insured. The advent of the blockchain, internet of things, cloud computing, and artificial intelligence provides a basis for enhancing the anti-fraud capability of the industry. In recent years, the application of Big Data and machine learning analysis methods in claims has significantly enhanced the ability to identify fraud. Traditional anti-fraud policies and rules utilize known fraud patterns. This approach is more effective in the absence of data or in a cold-start phase. As claim data accumulates, quantitative decision models based on machine learning and Big Data are often more effective in identifying fraud risks and optimizing claims flow. Compared with policy-based auditing, machine learning algorithms can simultaneously locate a variety of fraud to reduce unreasonable claims. For example, in auto insurance claims, the inherent relationship between maintenance items and accessories helps in calculating the risk probability of each index through the machine learning model. This helps to locate the corresponding claims, prompt the insurance companies to monitor the related service providers, surveyors and damage determination experts. In health insurance reimbursement we can analyze the reimbursement fraud record and predict excessive medical propensity according to the patient's past medical history and other relevant information.

AI-based image recognition can efficiently solve the problem of authenticity detection. This will open a vast space for anti-fraud applications in the insurance industry. For example, in the e-commerce sector insurers underwrite the freshness of online products such as whether a product is fresh when delivered. Consumers can submit photos of expired products as the primary basis for claims. Some consumers will cheat the system by searching for said pictures online as the base of their claim. It is neither easy nor efficient to identify such frauds manually. With image recognition and AI authentication technology, we can quickly identify fake photos with an accuracy of up to 95.7%. As another example, under the traditional life insurance annuity mode, even in case of the death of the policyholder, sometimes relatives do not notify the insurance company. Thus, they continue to receive the living allowances. Biometric recognition can help in identifying such frauds. Insurance companies can remotely judge the situation of the insured persons and effectively solve the problem of life-insurance frauds.

In the field of auto insurance claims, the integration of Big Data, IOT, blockchain and other technologies can prevent claims fraud. Using Big Data technology to monitor and analyze unusual purchases of spare parts, we can lock in suspicious customers and repair shops. The combination of the Internet of Things and blockchain technology can accurately track the real-time car operating data, driving records and driving path. Insurance companies can sense the uniqueness of the insured car. When an accident occurs, blockchain technology can faithfully record the accident time, place, handling time after the accident, etc., becoming an important basis for insurance companies to prevent frauds.

Intelligent operation mainly solves the problem of how to suggest the most suitable insurance product to a customer. Insurance products are a form of financial instrument that provide financial security to the insured person when problems arise. A variety of scenarios and all kinds of underwriting conditions make it difficult to standardize. This pain point creates an opportunity for smart products.

Smart operations include two applications - precise recommendation and intelligent interaction. These applications provide a precise and personalized tailor-made recommendation of insurance products, content, and services. To achieve this level, Big Data models analyze a person's lifespan, insurance awareness, and security status to form an accurate user profile. Then it selects the most optimal insurance product based on the needs of the customer. Along with the above capabilities, using NLP and multiple rounds of interactive capabilities, smart operations can involve intelligent Q&A, recommendations, or intelligent care. Smart operations enable a better understanding of the user's deepest needs, improve communication via smart chatbots, and eventually automate the insurance sales.

Looking into the future, with online insurance, the insurance industry aims to build personalized user experiences. In the future, policies will be simpler, pricing will be more reasonable, insurance products will be more diverse and of higher quality, and services will be simpler and more straightforward. Whether it is asset insurance or life insurance - pricing, insuring, risk management, anti-fraud, sales, and online interactions form the core of service chain. Blockchain, AI, security, IOT, and Cloud computing will enhance all these service elements. Blockchain will ensure transparency based on its history and immutable characteristics. AI will help with user interaction and speed up the claims process. Various security technologies will improve individual and asset identity management. IOT changes the way data exchanges. Auto and Health insurances will witness the first use of data exchanges. Cloud computing significantly reduces information costs in the insurance industry which allows small distributed claims to become a reality. All these technologies will lead insurance industry to a new era.

Conclusion

Smart insurance products, swift settlements, and automated advisory are becoming increasingly common in the insurance industry. Insurance providers should ride this wave of technology to improve operational efficiencies and meet customer expectations.